import random
import numpy as np
import os
import matplotlib.pyplot as plt
os.chdir("/home/labs/hornsteinlab/Collaboration/MOmaps_Noam/MOmaps")
from src.common.lib.image_sampling_utils import sample_images_all_markers_all_lines

path="/home/labs/hornsteinlab/Collaboration/MOmaps/input/images/processed/spd2/SpinningDisk/batch9_16bit_no_downsample/"
images = sample_images_all_markers_all_lines(path, _sample_size_per_markers=1,#*2, 
                                                 _num_markers=26, raw=False, all_conds=True)

for image_path in images:
    cur_marker = image_path.split('/')[-2]
    if cur_marker=='DAPI':
        continue
    cur_cell_line = image_path.split('/')[-4]
    cur_cond = image_path.split('/')[-3]
    image = np.load(image_path)
    no_tiles = image.shape[0]
    choose_tile = random.randint(0,no_tiles-1)
    fig, axs = plt.subplots(ncols=2)
    axs[0].imshow(image[choose_tile,:,:,0])
    axs[0].set_title(cur_marker)
    axs[0].axis('off')
    axs[1].imshow(image[choose_tile,:,:,1])
    axs[1].set_title('DAPI')
    axs[1].axis('off')
    plt.suptitle(f'{cur_cell_line} {cur_cond}',y=0.85)
    plt.show()

# save notebook as HTML ( the HTML will be saved in the same folder the original script is)
from IPython.display import display, Javascript
display(Javascript('IPython.notebook.save_checkpoint();'))
os.system('jupyter nbconvert --to html src/preprocessing/notebooks/cell_count_stats_analysis_microglia.ipynb')